Advanced Search-Term Disambiguation

ABSTRACT

A mechanism is provided for advanced search-term disambiguation. Responsive to detecting a search term being entered into an autocomplete search field of a search engine, a determination is made of a set of terms from a storage device upon which a search is to be performed. For each term in the set of terms, a determination is made of a semantic distance and independence (Sdi) score to each term candidate in a set of term candidates. The results of the semantic distance and independence (Sdi) scores are ranked and then pruned down to a. predetermined number of autocomplete results. A subset of term candidates associated with the predetermined number of autocomplete results from the set of term candidates is then presented to a user as autocomplete suggestions to the search.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for advancedsearch-term disambiguation.

The Internet is a global network of computers and networks joinedtogether by means of gateways that handle data transfer and theconversion of messages from a protocol of the sending network to aprotocol used by the receiving network. On the Internet, any computermay communicate with any other computer with information traveling overthe Internet through a variety of languages, also referred to asprotocols. The set of protocols used on the Internet is calledtransmission control protocol/Internet Protocol (TCP/IP).

The Internet has revolutionized communications and commerce, as well asbeing a source of both information and entertainment. With respect totransferring data over the Internet, the World Wide Web environment,also referred to simply as “the Web,” is used. The Web is a mechanismused to access information over the Internet. In the Web environment,servers and clients effect data transaction using the hypertext transferprotocol (HTTP), a known protocol for handling the transfer of variousdata files, such as text files, graphic images, animation files, audiofiles, and video files.

On the Web, the information in various data files is formatted forpresentation to a user by a standard page description language, thehypertext markup language (HTML). Documents using HTML are also referredto as Web pages. Web pages are connected to each other through links orhyperlinks. These links allow for a connection or link to other Webresources identified by a universal resource identifier (URI), such as auniform resource locator (URL).

A browser is a program used to look at and interact with all of theinformation on the Web. A browser is able to display Web pages and totraverse links to other Web pages, Resources, such as Web pages, areretrieved by a browser, which is capable of submitting a request for theresource. This request typically includes an identifier, such as, forexample, a URL As used herein, a browser is an application used tonavigate or view information or data in any distributed database, suchas the Internet or the World Wide Web.

Given the amount of information available through the World Wide Web,search engines have become valuable tools for finding content that isrelevant to a given user. A search engine is a software program or Website that searches a database and gathers and reports information thatcontains or is related to specified terms, However, given the vastamount of information on the Internet, search results often includemillions, or even tens of millions, of matching files, which arereferred to as “hits.” Many of these hits may be irrelevant to theuser's intended search. For example, if a user were to request a searchof the term “mercury,” the results could include hits related to theelement, the automobile manufacturer, the record label, the Roman god,the NASA manned spaceflight project, or some other category.

Once solution to this problem is to include more terms in the searchrequest to disambiguate the search. In the above example, the user mayrefine the search to include “mercury AND car.” However, it is up to theuser to determine which terms to add to refine the search.

One high tech solution is to use a clustering search engine, whichgroups results of the search into clusters. These search engines aremetasearch engines, which send user requests to several other searchengines and/or databases and return the results from each one. Theyallow users to enter their search criteria only one time and accessseveral search engines simultaneously.

A cluster is a group of similar topics that are related to the originalquery. The clusters are presented to the user through folders, The aimof this search engine technique is to organize numerous search resultsinto several meaningful categories (clusters). The user gets an overviewof the available themes or topics. Via one or two clicks on a folderand/or subfolders, the user may arrive at relevant search results thatwould be too far down in the ranking of a traditional search engine. Inaddition, the user may view similar results together in folders ratherthan scattered throughout a seemingly arbitrary list. While clusteringsearch engines organize results into categories, these categories arenaïve of the intention of the user. Given only a search query, no onecategory can be given a higher relevancy than any other. In addition,the algorithm used by a typical clustering engine produces humanreadable category names that may often be ambiguous themselves.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method, in a data processing system,is provided for advanced search-term disambiguation. The illustrativeembodiment determines a set of terms from a storage device upon which asearch is to be performed in response to detecting a search term beingentered into an autocomplete search field of a search engine. For eachterm in the set of terms, the illustrative embodiment determines asemantic distance and independence (S_(di)) score to each term candidatein a set of term candidates. The illustrative embodiment ranks resultsof the semantic distance and independence (S_(di)) scores. Theillustrative embodiment prunes the ranked results down to apredetermined number of autocomplete results. The illustrativeembodiment presents a subset of term candidates associated with thepredetermined number of autocomplete results from the set of termcandidates to a user as autocomplete suggestions to the search.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment,

These and other features and advantages of the present invention will hedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 depicts a functional block diagram of an advanced search-termdisambiguation mechanism in accordance with an illustrative embodiment;

FIG. 4 depicts one example of a hierarchical semantic graph generatedbetween terminologies associated with the term “mercury” from anenterprise storage, such as storage 304 of FIG. 3, in accordance with anillustrative embodiment;

FIGS. 5A and 5B illustrate the determined orthogonality for each of theidentified temis in keeping what the examples above in accordance withan illustrative embodiment; and

FIG. 6 depicts an exemplary flow diagram of the operation performed leyan advanced search-term disambiguation mechanism in accordance with anillustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for advanced search-termdisambiguation. As noted above, while clustering search engines mayorganize results into categories, these categories are naïve of theintention of the user. Currently, many of the clustering search enginesattempt to disambiguate the search through follow-up questions. Keepingwith the previous example using the term “mercury,” current clusteringsearch engines may issues a question to the user, such as “Did you meanmythology related to Mercury?” in order to disambiguate a search term“mercury”. However, other clustering search engines may use facets orconcepts to show groups of results that are related to a given concept(facet), such as matching concepts for “mercury”: mythical god (22),periodic table element (17), planet (12), etc. This clustering is thenfollowed by “relevance scoring” that indicates confidence in results:

About The God Mercury

-   -   Mercury is the son of Jupiter and Maia, one of the Pleiades.        Mercury is comparable to the Greek good Hermes... Relevance 100%

Toxicity of Mercury

-   -   Poisoning can result from mercury vapor inhalation, mercury        ingestion, mercury injection, and absorption of mercury through        the skin . . . Relevance 88%

Thus, search-term disambiguation that is performed after an initialsearch query that asks a question or requires the operator to pick froma list of choices, while successful, is gauged by a time to receivecorrect results where any activity that exceeds an arbitrary time limitmay result in user dissatisfaction or outright search abandonment (i.e.,a search process that takes more than 5 seconds). Further, while groupclustering and relevance scoring may work, the results of groupclustering and relevance scoring are only good until the results are notcorrect. That is, providing relevance of 100% for a totally wrong answerserves to hurt credibility and trust that may shorten the arbitrary timelimit that fosters user dissatisfaction or encourages searchabandonment.

The illustrative embodiments provide mechanisms for advanced search-termdisambiguation. That is, the mechanisms of the illustrative embodimentutilizes a measure of difference and orthogonality between related termsidentified from the enterprise database on which the search is beingperformed to automatically disambiguate the search terms before searchresults are shown. The mechanisms operate to disambiguate the searchterms as the initial search terms are being entered into the query. Bydisambiguating the search terms as they are being entered, the searchmay be performed correctly the first time and does not requirequestioning after the search has been performed or providing resultsbased on relevance that may not in fact be relevant at all. Thus, themechanisms of the illustrative embodiments provide more relevant resultsin less elapsed time, that in turn, provide better satisfaction and lesschance of search abandonment.

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

Thus, the illustrative embodiments may be utilized in many differenttypes of data processing environments. In order to provide a context forthe description of the specific elements and functionality of theillustrative embodiments, FIGS. 1 and 2 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 1 and 2 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 1 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 100 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 100 containsat least one network 102, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 100. The network 102may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 1 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

As shown in FIG. 1, one or more of the computing devices, e.g., server104, may be specifically configured to implement advanced search-termdisambiguation. The configuring of the computing device may comprise theproviding of application specific hardware, firmware, or the like tofacilitate the performance of the operations and generation of theoutputs described herein with regard to the illustrative embodiments.The configuring of the computing device may also, or alternatively,comprise the providing of software applications stored in one or morestorage devices and loaded into memory of a computing device, such asserver 104, for causing one or more hardware processors of the computingdevice to execute the software applications that configure theprocessors to perform the operations and generate the outputs describedherein with regard to the illustrative embodiments. Moreover, anycombination of application specific hardware, firmware, softwareapplications executed on hardware, or the like, may be used withoutdeparting from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is not a general purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates advanced search-term disambiguation.

As noted above, the mechanisms of the illustrative embodiments utilizespecifically configured computing devices, or data processing systems,to perform the operations for advanced search-term disambiguation. Thesecomputing devices, or data processing systems, may comprise varioushardware elements which are specifically configured, either throughhardware configuration, software configuration, or a combination ofhardware and software configuration, to implement one or more of thesystems/subsystems described herein. FIG. 2 is a block diagram of justone example data processing system in which aspects of the illustrativeembodiments may be implemented. Data processing system 200 is an exampleof a computer, such as server 104 in FIG. 1, in which computer usablecode or instructions implementing the processes and aspects of theillustrative embodiments of the present invention may be located and/orexecuted so as to achieve the operation, output, and external affects ofthe illustrative embodiments as described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SBACH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBMeServer™ System p® computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system200 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit 206. Alternatively, a single processorsystem may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 226 and loaded into memory, such as mainmemory 208, for executed by one or more hardware processors, such asprocessing unit 206, or the like. As such, the computing device shown inFIG. 2 becomes specifically configured to implement the mechanisms ofthe illustrative embodiments and specifically configured to perform theoperations and generate the outputs described hereafter with regard tothe advanced search-term disambiguation.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1 and 2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may he used inaddition to or in place of the hardware depicted in FIGS. 1 and 2. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

As noted above, the illustrative embodiments provide mechanisms foradvanced search-term disambiguation. The mechanisms operate todisambiguate the search terms as they are entered into the query. Bydisambiguating the search terms as they are being entered, the searchmay be performed correctly the first time and does not requirequestioning after the search has been performed or providing resultsbased on relevance that may not in fact be relevant at all. FIG. 3depicts a functional block diagram of an advanced search-termdisambiguation mechanism in accordance with an illustrative embodiment.Data processing system 300 comprises advanced search-term disambiguationmechanism 302 and storage 304. Storage 304 is a storage system uponwhich the query is to operate. Thus, the query performed by advancedsearch-term disambiguation mechanism 302 is limited to the data thatexists within storage 304.

Advanced search-term disambiguation mechanism 302 comprises semanticdistance determination engine 306, semantic orthogonality determinationengine 308, semantic distance and independence determination engine 310,and ranking and pruning engine 312. In order to disambiguate searchterms as they are being entered into data processing system 300,advanced search-term disambiguation mechanism 302 uses a measure ofdifference and orthogonality between related terms to decide whichdisambiguation concepts are to be presented to the user in anautocomplete list. That is, as user 314 enters terms into a search fieldof search engine 316 via graphical user interface 318, in order for aquery to be performed, advanced search-term disambiguation mechanism 302operates to disambiguate the concepts present in storage 304 so as topresent a disambiguated ranked list of terms based on the conceptspresent in storage 304 to user 318 via an autocomplete list featuresassociated with the search field. The goal is to present the mostdifferent terms from storage 304 to be shown autocomplete list (asopposed to most popular or most recently used). Because the autocompletelist comprises the most different search term options, the autocompletelist typically offers the most relevant result sets from fewer searchterms.

To provide an autocomplete list that offers the most relevant terms,semantic distance determination engine 306 searches storage 304 forterms related to an initial portion of the search terms being enteredinto the search field of search engine 316. In keeping with the previousexample, if user 314 initially enters the term “mercury” into the searchfield, semantic distance determination engine 306 searches storage 304for all entries, both terms and phrases, comprising the term “mercury”and identifies a parent identifier associated with each entry. Forexample, if the terms “Freddy Mercury” and “David Mercury” areidentified, then semantic distance determination engine 306 identifies aparent of each of these terms as “Person.” If the term “Mercury Plus®”is identified, then semantic distance determination engine 306identifies a parent of this term as “Product Name.” If just the term“mercury” is identified without any modifiers, then semantic distancedetermination engine 306 may identify the parent as being “Product,”“Element,” “Planet,” or “Mythology,” based on the parent in the taxonomyof where the term is identified. In the instant example, storage 304 isa storage device associated with an enterprise that sells a productnamed “Mercury Plus®” and utilizes the element “mercury” withincomponents of “products.” Thus, semantic distance determination engine306 identifies terms and phrases of children from different parents andcomputes the number of hops between the identified children, with morehops generally meaning a higher distance score.

FIG. 4 depicts one example of a hierarchical semantic graph generatedbetween terminologies associated with the term “mercury” from anenterprise storage, such as storage 304 of FIG. 3, in accordance with anillustrative embodiment. As is illustrated, Mercury Plus® 414 comprisesthe term “mercury” and is a direct child of Product Name 410. Thus, forMercury Plus® 414, semantic distance determination engine 306 of FIG. 3determines a distance score to Product Name 410 of one (1). As isfurther illustrated, Mercury 416 comprises the term “mercury” and is achild of Element 412 through Toxic 413. Thus, for Mercury 416, semanticdistance determination engine 306 would determine the number of hops(distance score) to Element 412 as two (2).

Returning to FIG. 3, once the distance scores are identified, semanticorthogonality determination engine 308 determines an orthogonality scorefor each identified entry. That is, semantic orthogonality determinationengine 308 determines a relationship between the identified entitiesusing the identified term or phrase regardless of dependency, especiallythose with the fewest possible paths indicating increased orthogonality.That is, excluding non-independent hops, from each parent, i.e. “ProductName” and “Element” in the current example, semantic orthogonalitydetermination engine 308 determines the number of hops to the entitiesidentified as having a relationship to the searched term, i.e.“mercury.” FIGS. 5A and 5B illustrate the determined orthogonality foreach of the identified terms in keeping what the examples above inaccordance with an illustrative embodiment. In FIG. 5A, semanticorthogonality determination engine 308 determines orthogonality scoresfor Product Name 510 as:

-   -   one (1) between Product Name 510 and Mercury Plus®514,    -   three (3) between Product Name 510 and Routers 504, the hop        between Mercury Plus®514 and Mercury 516 is counted as one hop        because Phones 502 is not singularly dependent (i.e. not        independent),    -   three (3) between Product Name 510 and Appliances 506, the hop        between Mercury Plus®514 and Mercury 516 is counted as one hop        because Phones 502 is not singularly dependent (i.e. not        independent), and    -   four (4) between Product Name 510 and Utensils 508, the hop        between Mercury Plus®514 and Mercury 516 is counted as one hop        because Phones 502 is not singularly dependent (ie. not        independent) and the hop between Appliances 506 and Utensils 508        is counted as one hop because Housewares 518 is not singularly        dependent (i.e. not independent).

In FIG. 5B, semantic orthogonality determination engine 308 determinesorthogonality scores for Element 512 as:

-   -   three (3) between Element 512 and Mercury Plus®514, the hop        between Mercury Plus®514 and Mercury 516 is counted as one hop        because Phones 502 is not singularly dependent i.e. not        independent),    -   three (3) between Element 512 and Routers 504,    -   three (3) between Element 512 and Appliances 506, and    -   four (4) between Element 512 and Utensils 508, the hop between        Appliances 506 and Utensils 508 is counted as one hop because        Housewares 518 is not singularly dependent (i.e. not        independent).

Once the distance scores and orthogonality scores are identified areidentified, semantic distance and independence determination engine 310determines a semantic distance and independence (S_(di)) score for termst: D(t,ai) which is a function that measures semantic distance (measureof opposite terms) for given term t and potential term candidate to beoffered aj multiplied by O(t,ai) which is a function that measuressemantic orthogonality (most independent) for given term t and potentialterm candidate to be offered aj, which in formula form is described as:

S _(di)=(D(t,aj)*O(t, aj)).

Once executed for each term and potential term candidate, semanticdistance and independence determination engine 310 provides a list ofterms that are most different based on proximity to distant terms(including implied aliases) and greatest independence from terms typedthus tier in the search field. With regard to the examples providedabove in FIGS. 4 and 5A (Product Name), the results would be:

-   -   Mercury Plus® 514—Sdi=1*1=1,    -   Routers 504—Sdi=1*3 =3,    -   Appliances 506—Sdi=1*3 3, and    -   Utensils 508—Sdi=1*4=4.        With regard to the examples provided above in FIGS. 4 and 5B        (Element), the results would be:    -   Mercury Plus®514—Sdi=2*3=6,    -   Routers 504—Sdi=2*3=6,    -   Appliances 506—Sdi=2*3=6, and    -   Utensils 508—Sdi=2*4=8.

With the semantic distance and independence values identified, rankingand pruning engine 312 ranks all of the scores and presents terms orphrases associated with the predetermined number of results to user 314as autocomplete suggestions to the query being entered by user 314. Thepredetermined number of result may be user identified or automaticallydetermined based on, for example, a ratio associated with identifiedcandidate terms, a number candidate terms above an identified threshold,or the like. For example, using the above example and if thepredetermined number of suggestions were four (4), ranking and pruningengine 312 would provide for the initial term “mercury” autofillsuggestions or

-   -   Mercury Plus®,    -   Routers,    -   Appliances, and    -   Utensils.

Thus, the illustrative embodiments provide mechanisms for advancedsearch-term disambiguation through a measure of difference andorthogonality between related terms identified from the enterprisedatabase. As the initial search term(s) are being entered into thequery, the illustrative embodiments provide more relevant results inless elapsed time, that in turn, provide better satisfaction and lesschance of search abandonment.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smailtalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the userscomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider),in some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 6 depicts an exemplary flow diagram of the operation performed byan advanced search-term disambiguation mechanism in accordance with anillustrative embodiment. As the operation begins, the advancedsearch-term disambiguation mechanism detects a search term being enteredinto an autocomplete search field of a search engine (step 602). Theadvanced search-term disambiguation mechanism searches an associatedenterprise storage system for one or more terms or phrases related tothe entered search term (step 604). For each identified term or phrase,the advanced search-term disambiguation mechanism determines a distancescore (i.e. number of hops)) from the term or phrase to a highesthierarchical parent associated with the term or phrase (step 606). Foreach identified term or phrase, the advanced search-term disambiguationmechanism identifies an orthogonality scores (i.e. a number ofindependent hops) from each highest hierarchical parent to the searchterm or phrase (step 608).

The advanced search-term disambiguation mechanism then identifies, foridentified terms or phrases t, a semantic distance and independence(S_(di)) score (step 610):

S _(di)=(D(t,aj)*O(t, aj))

where D(t,ai) which is a function that measures semantic distancemeasure of opposite terms) for given term t and potential term candidateto be offered aj multiplied by O(t,ai) which is a function that measuressemantic orthogonality (most independent) for given term t and potentialterm candidate to be offered aj. The advanced search-term disambiguationmechanism then ranks the results of the semantic distance andindependence (S_(di)) scores (step 612) and prunes the ranked resultsdown to a predetermined number of autocomplete results (step 614). Theadvanced search-term disambiguation mechanism then presents terms orphrases associated with the predetermined number of results to the useras autocomplete suggestions to the query being entered by the user (step616), with the operation terminating thereafter.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments provide mechanisms for advancedsearch-term disambiguation. The mechanisms of the illustrativeembodiment utilizes a measure of difference and orthogonality betweenrelated terms identified from the enterprise database on which thesearch is being performed to automatically disambiguate the search termsbefore search results are shown. The mechanisms operate to disambiguatethe search terms as the initial search terms are being entered into thequery. By disambiguating the search terms as they are being entered, thesearch may be performed correctly the first time and does not requirequestioning after the search has been performed or providing resultsbased on relevance that may not in fact be relevant at all. Thus, themechanisms of the illustrative embodiments provide more relevant resultsin less elapsed time, that in turn, provide better satisfaction and lesschance of search abandonment.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like,

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Moderns, cable moderns and Ethernet cards are just a few ofthe currently available types of network adapters for wiredcommunications. Wireless communication based network adapters may alsobe utilized including, but not limited to, 802.11 a/b/g/n wirelesscommunication adapters, Bluetooth wireless adapters, and the like. Anyknown or later developed network adapters are intended to be within thespirit and scope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system, foradvanced search-term disambiguation, the method comprising: responsiveto detecting a search term being entered into an autocomplete searchfield of a search engine, determining a set of terms from a storagedevice upon which a search is to be performed; for each term in the setof terms, determining a semantic distance and independence (S_(d)) scoreto each term candidate in a set of term candidates; ranking results ofthe semantic distance and independence (S_(di)) scores; pruning theranked results down to a predetermined number of autocomplete results;and presenting a subset of term candidates associated with thepredetermined number of autocomplete results from the set of termcandidates to a user as autocomplete suggestions to the search.
 2. Themethod of claim 1, wherein the semantic distance and independence(S_(di)) score is determined using the following function:S _(di)=(D(t,aj)*O(t, aj)) where D(t,aj) which is a function thatmeasures semantic distance (measure of opposite terms) for given term tand potential term candidate to be offered aj multiplied by O(t,aj)which is a function that measures semantic orthogonality (mostindependent) for given term t and potential term candidate to be offeredaj.
 3. The method of claim 2, wherein the semantic distance is adistance score (i.e. number of hops) from an occurrence of the term to ahighest hierarchical parent associated with the term.
 4. The method ofclaim 2, wherein the semantic orthogonality is an orthogonality score(i.e. a number of independent hops) from each highest hierarchicalparent one or more entities identified as having a relationship to thesearch term.
 5. The method of claim 1, wherein the storage device is anenterprise storage device.
 6. The method of claim 1, wherein the subsetof terms candidate are most different terms associated with the searchterm in the storage device as identified by semantic distance andsemantic orthogonality.
 7. The method of claim 1, wherein thepredetermined number of autocomplete results are at least one of useridentified, automatically determined based on a ratio associated withidentified candidate terms, or automatically determined based on anumber of candidate terms above an identified threshold.
 8. A computerprogram product comprising a computer readable storage medium having acomputer readable program stored therein, wherein the computer readableprogram, when executed on a computing device, causes the computingdevice to: responsive to detecting a search term being entered into anautocomplete search field of a search engine, determine a set of termsfrom a storage device upon which a search is to be performed; for eachterm in the set of terms, determine a semantic distance and independence(S_(di)) score to each term candidate in a set of term candidates; rankresults of the semantic distance and independence (S_(di)) scores; prunethe ranked results down to a predetermined number of autocompleteresults; and present a subset of term candidates associated with thepredetermined number of autocomplete results from the set of termcandidates to a user as autocomplete suggestions to the search.
 9. Thecomputer program product of claim 8, wherein the semantic distance andindependence (S_(di)) score is determined using the following function:S _(di)=(D(t,aj)*O(t, aj)) where D(t,aj) which is a function thatmeasures semantic distance (measure of opposite terms) for given term tand potential term candidate to be offered aj multiplied by O(t,aj)which is a function that measures semantic orthogonality (mostindependent) for given term t and potential term candidate to be offeredaj.
 10. The computer program product of claim 9, wherein the semanticdistance is a distance score (i.e. number of hops) from an occurrence ofthe term to a highest hierarchical parent associated with the term. 11.The computer program product of claim 9, wherein the semanticorthogonality is an orthogonality score (i.e. a number of independenthops) from each highest hierarchical parent one or more entitiesidentified as having a relationship to the search term.
 12. The computerprogram product of claim 8, wherein the storage device is an enterprisestorage device.
 13. The computer program product of claim 8, wherein thesubset of terms candidate are most different terms associated with thesearch term in the storage device as identified by semantic distance andsemantic orthogonality.
 14. The computer program product of claim 8,wherein the predetermined number of autocomplete results are at leastone of user identified, automatically determined based on a ratioassociated with identified candidate terms, or automatically determinedbased on a number of candidate terms above an identified threshold. 15.An apparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory comprises instructions which, whenexecuted by the processor, cause the processor to: responsive todetecting a search term being entered into an autocomplete search fieldof a search engine, determine a set of terms from a storage device uponwhich a search is to be performed; for each term in the set of terms,determine a semantic distance and independence (S_(di)) score to eachterm candidate in a set of term candidates; rank results of the semanticdistance and independence (S_(di)) scores; prune the ranked results downto a predetermined number of autocomplete results; and present a subsetof term candidates associated with the predetermined number ofautocomplete results from the set of term candidates to a user asautocomplete suggestions to the search,
 16. The apparatus of claim 15,wherein the semantic distance and independence (S_(di)) score isdetermined using the following function:S _(di)=(D(t,aj)*O(t, aj)) where D(t,aj) which is a function thatmeasures semantic distance (measure of opposite terms) for given term tand potential term candidate to be offered aj multiplied by O(t,aj)which is a function that measures semantic orthogonality (mostindependent) for given term t and potential term candidate to be offeredaj.
 17. The apparatus of claim 16, wherein the semantic distance is adistance score (i.e. number of hops) from an occurrence of the term to ahighest hierarchical parent associated with the term.
 18. The apparatusof claim 16, wherein the semantic orthogonality is an orthogonalityscore (i.e. a number of independent hops) from each highest hierarchicalparent one or more entities identified as having a relationship to thesearch term.
 19. The apparatus of claim 15, wherein the subset of termscandidate are most different terms associated with the search term inthe storage device as identified by semantic distance and semanticorthogonality.
 20. The apparatus of claim 15, wherein the predeterminednumber of autocomplete results are at least one of user identified,automatically determined based on a ratio associated with identifiedcandidate terms, or automatically determined based on a number ofcandidate terms above an identified threshold.